{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "# setting params\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (30, 10),\n",
    "          'axes.labelsize': 'x-large',\n",
    "          'axes.titlesize':'x-large',\n",
    "          'xtick.labelsize':'x-large',\n",
    "          'ytick.labelsize':'x-large'}\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "pd.options.display.max_colwidth = 600\n",
    "\n",
    "df = pd.read_csv(\"day.csv\")\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(df[\"temp\"],bins=30,kde=True)\n",
    "plt.xlabel(\"temp\",fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a219f38d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10e815240>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x10e83c8d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a21b8df60>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numerical_features = ['temp','atemp','hum','windspeed']\n",
    "df[numerical_features].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1ae04898>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data=df[['yr', 'cnt']], x=\"yr\",y=\"cnt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 421.09x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['date'] = pd.to_datetime(df['dteday'])\n",
    "df['dayofyear'] = df[\"date\"].dt.dayofyear \n",
    "sns.lmplot(\"dayofyear\",\"cnt\",data=df,hue=\"yr\",fit_reg=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a9bab70>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data=df[['season', 'cnt']], x=\"season\",y=\"cnt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'weekday distribution of counts')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sns.barplot(data=df[['mnth', 'cnt']], x=\"mnth\",y=\"cnt\")\n",
    "ax.set(title=\"Monthly distribution of counts\")\n",
    "fig,ax = plt.subplots()\n",
    "sns.barplot(data=df[['weekday', 'cnt']], x=\"weekday\",y=\"cnt\")\n",
    "ax.set(title=\"weekday distribution of counts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a239f2208>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatt = df[[\"dteday\",\"season\",\"mnth\",\"holiday\",\"weekday\",\"workingday\",\"weathersit\",\"temp\",\"atemp\",\"hum\",\"windspeed\", \"cnt\"]].corr()\n",
    "mask = np.array(corrMatt)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "sns.heatmap(corrMatt, mask=mask,\n",
    "           vmax=.8, square=True,annot=True)\n",
    "\n",
    "#可以看到cnt和天气负相关（值越大天气越不好）、湿度、风速负相关；和温度有较高相关，和季节有一定相关；神奇的是：和是否工作日、星期几无关。（通过跑subplots也看的出来）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>mnth_1</th>\n",
       "      <th>mnth_2</th>\n",
       "      <th>mnth_3</th>\n",
       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>mnth_6</th>\n",
       "      <th>...</th>\n",
       "      <th>weathersit_1</th>\n",
       "      <th>weathersit_2</th>\n",
       "      <th>weathersit_3</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  mnth_4  \\\n",
       "0         1         0         0         0       1       0       0       0   \n",
       "1         1         0         0         0       1       0       0       0   \n",
       "2         1         0         0         0       1       0       0       0   \n",
       "3         1         0         0         0       1       0       0       0   \n",
       "4         1         0         0         0       1       0       0       0   \n",
       "\n",
       "   mnth_5  mnth_6    ...      weathersit_1  weathersit_2  weathersit_3  \\\n",
       "0       0       0    ...                 0             1             0   \n",
       "1       0       0    ...                 0             1             0   \n",
       "2       0       0    ...                 1             0             0   \n",
       "3       0       0    ...                 1             0             0   \n",
       "4       0       0    ...                 1             0             0   \n",
       "\n",
       "   weekday_0  weekday_1  weekday_2  weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0          0          0          0          0          0          0          1  \n",
       "1          1          0          0          0          0          0          0  \n",
       "2          0          1          0          0          0          0          0  \n",
       "3          0          0          1          0          0          0          0  \n",
       "4          0          0          0          1          0          0          0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#特征工程\n",
    "categorical_features = ['season','mnth','weathersit','weekday']\n",
    "#数据类型变为object，才能被get_dummies处理\n",
    "for col in categorical_features:\n",
    "    df[col] = df[col].astype('object')\n",
    "    \n",
    "X_train_cat = df[categorical_features]\n",
    "X_train_cat = pd.get_dummies(X_train_cat)\n",
    "X_train_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>mnth_1</th>\n",
       "      <th>mnth_2</th>\n",
       "      <th>mnth_3</th>\n",
       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>mnth_6</th>\n",
       "      <th>...</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.355170</td>\n",
       "      <td>0.373517</td>\n",
       "      <td>0.828620</td>\n",
       "      <td>0.284606</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.379232</td>\n",
       "      <td>0.360541</td>\n",
       "      <td>0.715771</td>\n",
       "      <td>0.466215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.144830</td>\n",
       "      <td>0.449638</td>\n",
       "      <td>0.465740</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.175530</td>\n",
       "      <td>0.174649</td>\n",
       "      <td>0.607131</td>\n",
       "      <td>0.284297</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.209120</td>\n",
       "      <td>0.197158</td>\n",
       "      <td>0.449313</td>\n",
       "      <td>0.339143</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  mnth_4  \\\n",
       "0         1         0         0         0       1       0       0       0   \n",
       "1         1         0         0         0       1       0       0       0   \n",
       "2         1         0         0         0       1       0       0       0   \n",
       "3         1         0         0         0       1       0       0       0   \n",
       "4         1         0         0         0       1       0       0       0   \n",
       "\n",
       "   mnth_5  mnth_6     ...      weekday_3  weekday_4  weekday_5  weekday_6  \\\n",
       "0       0       0     ...              0          0          0          1   \n",
       "1       0       0     ...              0          0          0          0   \n",
       "2       0       0     ...              0          0          0          0   \n",
       "3       0       0     ...              0          0          0          0   \n",
       "4       0       0     ...              1          0          0          0   \n",
       "\n",
       "       temp     atemp       hum  windspeed  holiday  workingday  \n",
       "0  0.355170  0.373517  0.828620   0.284606        0           0  \n",
       "1  0.379232  0.360541  0.715771   0.466215        0           0  \n",
       "2  0.171000  0.144830  0.449638   0.465740        0           1  \n",
       "3  0.175530  0.174649  0.607131   0.284297        0           1  \n",
       "4  0.209120  0.197158  0.449313   0.339143        0           1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数值型变量预处理，\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mn_X = MinMaxScaler()\n",
    "numerical_features = ['temp','atemp','hum','windspeed']\n",
    "temp = mn_X.fit_transform(df[numerical_features])\n",
    "\n",
    "X_train_num = pd.DataFrame(data=temp, columns=numerical_features, index =df.index)\n",
    "#X_train_num.head()\n",
    "\n",
    "#X_train = pd.concat([X_train_cat, df[\"temp\"],df[\"atemp\"],df[\"hum\"],df[\"windspeed\"]], axis = 1, ignore_index=False)\n",
    "X_train = pd.concat([X_train_cat, X_train_num, df['holiday'],  df['workingday']], axis = 1, ignore_index=False)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>mnth_1</th>\n",
       "      <th>mnth_2</th>\n",
       "      <th>mnth_3</th>\n",
       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>...</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>yr</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.355170</td>\n",
       "      <td>0.373517</td>\n",
       "      <td>0.828620</td>\n",
       "      <td>0.284606</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.379232</td>\n",
       "      <td>0.360541</td>\n",
       "      <td>0.715771</td>\n",
       "      <td>0.466215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.144830</td>\n",
       "      <td>0.449638</td>\n",
       "      <td>0.465740</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.175530</td>\n",
       "      <td>0.174649</td>\n",
       "      <td>0.607131</td>\n",
       "      <td>0.284297</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.209120</td>\n",
       "      <td>0.197158</td>\n",
       "      <td>0.449313</td>\n",
       "      <td>0.339143</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant  season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  \\\n",
       "0        1         1         0         0         0       1       0       0   \n",
       "1        2         1         0         0         0       1       0       0   \n",
       "2        3         1         0         0         0       1       0       0   \n",
       "3        4         1         0         0         0       1       0       0   \n",
       "4        5         1         0         0         0       1       0       0   \n",
       "\n",
       "   mnth_4  mnth_5  ...   weekday_5  weekday_6      temp     atemp       hum  \\\n",
       "0       0       0  ...           0          1  0.355170  0.373517  0.828620   \n",
       "1       0       0  ...           0          0  0.379232  0.360541  0.715771   \n",
       "2       0       0  ...           0          0  0.171000  0.144830  0.449638   \n",
       "3       0       0  ...           0          0  0.175530  0.174649  0.607131   \n",
       "4       0       0  ...           0          0  0.209120  0.197158  0.449313   \n",
       "\n",
       "   windspeed  holiday  workingday  yr   cnt  \n",
       "0   0.284606        0           0   0   985  \n",
       "1   0.466215        0           0   0   801  \n",
       "2   0.465740        0           1   0  1349  \n",
       "3   0.284297        0           1   0  1562  \n",
       "4   0.339143        0           1   0  1600  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FE_train = pd.concat([df['instant'], X_train,  df['yr'],df['cnt']], axis = 1)\n",
    "FE_train.to_csv('FE_day.csv', index=False)\n",
    "FE_train.head()\n",
    "#FE_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "X_train_set,X_test_set, y_train_set, y_test_set = train_test_split(X_train,df['cnt'],test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 584 entries, 97 to 684\n",
      "Data columns (total 32 columns):\n",
      "season_1        584 non-null uint8\n",
      "season_2        584 non-null uint8\n",
      "season_3        584 non-null uint8\n",
      "season_4        584 non-null uint8\n",
      "mnth_1          584 non-null uint8\n",
      "mnth_2          584 non-null uint8\n",
      "mnth_3          584 non-null uint8\n",
      "mnth_4          584 non-null uint8\n",
      "mnth_5          584 non-null uint8\n",
      "mnth_6          584 non-null uint8\n",
      "mnth_7          584 non-null uint8\n",
      "mnth_8          584 non-null uint8\n",
      "mnth_9          584 non-null uint8\n",
      "mnth_10         584 non-null uint8\n",
      "mnth_11         584 non-null uint8\n",
      "mnth_12         584 non-null uint8\n",
      "weathersit_1    584 non-null uint8\n",
      "weathersit_2    584 non-null uint8\n",
      "weathersit_3    584 non-null uint8\n",
      "weekday_0       584 non-null uint8\n",
      "weekday_1       584 non-null uint8\n",
      "weekday_2       584 non-null uint8\n",
      "weekday_3       584 non-null uint8\n",
      "weekday_4       584 non-null uint8\n",
      "weekday_5       584 non-null uint8\n",
      "weekday_6       584 non-null uint8\n",
      "temp            584 non-null float64\n",
      "atemp           584 non-null float64\n",
      "hum             584 non-null float64\n",
      "windspeed       584 non-null float64\n",
      "holiday         584 non-null int64\n",
      "workingday      584 non-null int64\n",
      "dtypes: float64(4), int64(2), uint8(26)\n",
      "memory usage: 46.8 KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "97     1.583806\n",
       "503    1.941518\n",
       "642    1.932493\n",
       "498    1.893305\n",
       "303    1.782274\n",
       "Name: cnt, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_set.info()\n",
    "#y_train_set.head()\n",
    "log_y_train_set = np.log10(np.sqrt(y_train_set))\n",
    "log_y_test_set = np.log10(np.sqrt(y_test_set))\n",
    "log_y_train_set.head()\n",
    "#X_test_set.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.008\n",
      "RMSE: 0.089\n",
      "预测linearRegression性能得分: 0.54\n",
      "预测ridgeRegression性能得分: 0.52\n",
      "预测lassoRegression性能得分: 0.51\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "linearRegression = linear_model.LinearRegression()\n",
    "linearRegression.fit(X_train_set, log_y_train_set)\n",
    "#linearRegression.fit(X_train_set, y_train_set)\n",
    "    \n",
    "print('Mean squared error: %.3f' % mean_squared_error(log_y_test_set,linearRegression.predict(X_test_set)))\n",
    "print('RMSE: %.3f' % np.sqrt(mean_squared_error(log_y_test_set,linearRegression.predict(X_test_set))))\n",
    "#print('Mean squared error: %.3f' % mean_squared_error(y_test_set,linearRegression.predict(X_test_set)))\n",
    "\n",
    "#计算预测性能得分\n",
    "print(\"预测linearRegression性能得分: %.2f\" % linearRegression.score(X_test_set, log_y_test_set))\n",
    "\n",
    "ridgeRegression = linear_model.Ridge()\n",
    "ridgeRegression.fit(X_train_set, y_train_set)\n",
    "print(\"预测ridgeRegression性能得分: %.2f\" % ridgeRegression.score(X_test_set, y_test_set))\n",
    "\n",
    "lassoRegression = linear_model.Lasso()\n",
    "lassoRegression.fit(X_train_set, y_train_set)\n",
    "print(\"预测lassoRegression性能得分: %.2f\" % lassoRegression.score(X_test_set, y_test_set))\n",
    "\n",
    "#通过将y值log后，Mean squared error从20万降到了0.008.\n",
    "#但以上三种模型得分都不高（去掉关联不强的是否工作日、星期几的话，得分会更低。于是目前选取的几乎是全量特征了），比较不解，还请老师解答，十分感谢~ "
   ]
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   "outputs": [],
   "source": []
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